An application of artificial neural networks for rainfall forecasting

By K. Luk, J. Ball, Ashish Sharma. 2001

320 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/be18b41a763997e7e3aca065749442394569e0d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. F. Mekanik
2. S. Gato-Trinidad
3. Mohammad Valipour
4. Monzur Alam Imteaz
5. Amgad Elmahdi

In Environmental Engineering

1. F. Mekanik
2. S. Gato-Trinidad
3. Mohammad Valipour
4. Monzur Alam Imteaz
5. Amgad Elmahdi
6. Ravinesh C. Deo
7. Amir Mosavi
8. Kwok-Wing Chau

In Global and Planetary Change

1. Mohammad Valipour
2. Monzur Alam Imteaz
3. Ravinesh C. Deo
4. Mehmet Sahin
5. Christian Thiel
6. Gillian Harrison

Citations in the corpus

• How Much Meteorological Information Is Necessary to Achieve Reliable Accuracy for Rainfall Estimations (2016, background): “[19] studied an application of artificial neural networks (ANNs) for rainfall forecasting successfully.”
• Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, methodology): “It has also been used for many cases of rainfall forecasting (Hsu et al., 1995; Luk et al., 2001; Mekanik et al., 2011; Ramírez et al., 2005; Toth et al., 2000).”